An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time Sequences

Abstract

Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target’s location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5–8.5 s for the transition between states and by approximately 24 s for the entire process.

Research on visible light indoor positioning algorithm based on fire safety

“…The camera is located in the center of the room but the angle is rotated. -----------------------atan = coordinates of the center point M are (160, 120), and the distance l 1 between A' and the center point M is as follows: (7) The distance l 2 between C' and A' is as follows: (8) The distance l 3 between C' and the center point M is as follows: (9) Parameter β can be calculated from the cosine theorem (10) (11) Assuming that the actual motion step size of the lens is proportional to l 3 , the actual lens movement distance and the pixel point correspondence are as follows: (12) where L is the distance that the actual lens moves in space, l 3 is the distance between C' and the center point M, and n is a constant.…”

“…After determining the use of a certain indoor positioning technology, it is necessary to introduce an effective positioning algorithm to achieve the expected indoor positioning accuracy. The existing indoor positioning algorithms include the LANDMARC algorithm using RFID technology [4]; the GIPS algorithm applied to university campuses and large indoor shopping malls [5]; the passive infrared sensor tracking localization algorithm for smart home service systems [6]; and the indoor co-location algorithm for emergency situations [7] and car indoor positioning algorithm [8], etc. These latest positioning algorithms have greater precision in their respective application scenarios.…”